Real-Time Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified interconnected in how they process, interpret, and extract value from data. Together, they enable efficient data processing, enhance decision-making, and improve user experiences: Analytics transforms raw architecture teams must rethink traditional data infrastructures. The future lies in Real-Time Unified Data Layers—platforms that seamlessly support analytics, search, and AI workloads at scale. These systems break

Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu adrian.stanciu.pub@gmail.com CppCon, 2023 1 / 50About me ◮ I am a software engineer from Romania ◮ I have data to make predictions 11 / 50Neural network (NN) 13 / 50Convolutional neural network (CNN) ◮ Efficient in image classification ◮ A convolutional layer can apply filters to detect: ◮ Edges ◮ Shapes ◮ 50Conclusions ◮ Code isn’t enough... data matters ◮ More diverse data leads to better models ◮ Building accurate models is an expert job ◮ Running on-device inference is straightforward ◮ Running on-device

digital datasets that have been in the making for over three decades; breakthrough large language models (LLMs) that – in effect – found freedom with the November 2022 launch of OpenAI’s ChatGPT with computers are ingesting massive datasets to get smarter and more competitive. Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making are racing to build and deploy the next layers of AI infrastructure: agentic interfaces, enterprise copilots, real-world autonomous systems, and sovereign models. Rapid advances in artificial intelligence

library aims to provide a simple yet powerful framework for building and training machine learning models. By leveraging the performance efficiency of C++, micro- grad++ offers a robust solution for integrating for defining layers and neurons, enabling users to construct complex network architec- tures. • Backpropagation: The implementation of backpropa- gation in micrograd++ allows for efficient training of of models through gradient descent. • Gradient Clipping: To prevent the issue of exploding gradients, micrograd++ incorporates gradient clipping, ensuring stable training processes. C. Unique Aspects and

Windows (none) AI contributions Contributions must not include content generated by large language models or other probabilistic tools, including but not limited to Copilot or ChatGPT. This policy covers contributions, something that we cannot trust an AI tool to do. Copyright issues: Publicly available models are trained on copyrighted content, both accidentally and intentionally, and their output often includes Ethical issues: AI tools require an unreasonable amount of energy and water to build and operate, their models are built with heavily exploited workers in unacceptable working conditions, and they are being used

conditions of tremendous uncertainty, your choices will often turn out to be wrong.Agile and plan-driven models have very different ways of dealing with uncertainty. Plan driven approaches, even Waterfall, have more quickly and with more good information available.  “Failing” in this sense is simply an efficient process we use to select among alternatives. Shadow IT Agile ways of working support a community done: The hierarchy must be flattened. Layers of management get in the way of goals. The employee wants the shortest possible path to shipping code without needing layers of approval. Management should be

-Setting the game size in pixels -Tiled basic usage -Tile layers and object layers -Tilemaps -Creating sprites from object layers -Moving a character in a top-down level Zenva Academy – Online reserved Page 25 The next step is to create the Layers. A level will have different layers that sit on top of each other. The naming of the layers is important here as we need to refer to that in the types of layers: -Tile layer: layer made of tiles/blocks. -Objects layer: layer where you create vector objects that can contain metadata. You can create layers using the “+” button under layers, to

provides �→ more relevant search results. As one of the core functions of AI and �→ large language models (LLMs), vector search can be used in various �→ scenarios such as Retrieval-Augmented Generation which provides more relevant search results. As one of the core functions of AI and large language models (LLMs), vector search can be used in various scenarios such as Retrieval-Augmented Generation (RAG) IntegerField(default=0) class Meta: table_name = "players" For more information, refer to peewee documentation: Models and Fields. Insert data #### Insert a single record Player.create(name="test", coins=100, goods=100)

provides �→ more relevant search results. As one of the core functions of AI and �→ large language models (LLMs), vector search can be used in various �→ scenarios such as Retrieval-Augmented Generation which provides more relevant search results. As one of the core functions of AI and large language models (LLMs), vector search can be used in various scenarios such as Retrieval-Augmented Generation (RAG) IntegerField(default=0) class Meta: table_name = "players" For more information, refer to peewee documentation: Models and Fields. Insert data #### Insert a single record Player.create(name="test", coins=100, goods=100)